Year of Award

2021

Document Type

Thesis

Degree Type

Master of Science (MS)

Degree Name

Computer Science

Department or School/College

Computer Science

Committee Chair

Douglas Brinkerhoff

Committee Co-chair

Jesse Johnson

Commitee Members

Tony Meirbachtol

Keywords

computer science, glaciology, geophysics, bayesian statistics, climate science, computer vision, machine learning

Publisher

University of Montana

Subject Categories

Artificial Intelligence and Robotics | Climate | Data Science | Glaciology | Hydrology | Numerical Analysis and Computation | Numerical Analysis and Scientific Computing | Partial Differential Equations

Abstract

Using time dependent observations derived from terrestrial LiDAR and oblique
time-lapse imagery, we demonstrate that a Bayesian approach to glacial motion es-
timation provides a concise way to incorporate multiple data products into a single
motion estimation procedure effectively producing surface velocity estimates with
an associated uncertainty. This approach brings both improved computational effi-
ciency, and greater scalability across observational time-frames when compared to
existing methods. To gauge efficacy, we apply these methods to a set of observa-
tions from the Helheim Glacier, a critical actor in contemporary mass loss trends
observed in the Greenland Ice Sheet. We find that the Helheim glacier exhibits
a mean velocity of approximately 19md−1 and discuss the implications of these
methods as they pertain to ongoing efforts to characterize the Greenland Ice Sheet
and its contributions to global mean sea level rise.

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© Copyright 2021 Franklyn T. Dunbar II